1. Field of the Invention
The present invention generally relates to multiple-input-multiple-output (MIMO) communication systems, and particularly relates to estimating signal impairment correlations in such systems.
2. Background
Generalized Rake (G-Rake) receivers, chip equalization receivers, and other types of interference-suppressing receivers estimate received signal impairment correlations as a basis for whitening colored interference. For example, a “parametric” G-Rake receiver models received signal impairment correlations using a number of model terms. More particularly, the parametric G-Rake receiver expresses different signal impairment correlations using corresponding covariance matrices, e.g., a covariance matrix modeling same-cell interference, a covariance matrix modeling other-cell interference, etc. Each matrix appears as a term in an overall impairment correlation model and, in general, each term includes a scaling factor, also referred to as a “fitting” parameter.
In operation, the parametric G-Rake directly estimates received signal impairment correlations from its observation of Common Pilot Channel (CPICH) samples, for example. The modeled impairment correlations are then “fitted” to the directly observed impairments, based on a least squares or other fitting process that adjusts the fitting parameter of each term in the impairment correlation model. The fitting process works relatively well despite the potentially noisy direct estimates of signal impairment correlations taken from the CPICH samples, because there are relatively few terms in the impairment correlation model, and therefore relatively few fitting parameters to be determined during the fitting process.
In contrast, in Multiple-Input-Multiple-Output (MIMO) systems, a potentially much larger number of fitting parameters must be determined from these same direct estimates, because of the more complicated impairment correlation models attendant with operation in MIMO systems. Extending the “standard” parametric G-Rake process of determining fitting parameters therefore suffers because of the larger number of model terms to account for in MIMO contexts, such as in the Dual Transmit Antenna Array (D-TxAA) MIMO context of Release 7 of the Wideband CDMA (WCDMA) standards.
Moreover, MIMO contexts brings with them particular forms of signal impairment that differ between the data and pilot signals, complicating use of pilot-based impairment correlation estimations in the impairment model fitting process. For example, data signals in MIMO systems may suffer from cross-stream interference arising from the reuse of channelization parameters (e.g., channel code or channel frequency) for different information streams at the MIMO transmitter. Such interference generally does not arise on pilot signals because they are transmitted across MIMO antennas using unique channelization parameters, and the pilot-based direct observations of received signal impairment correlations therefore do not reflect the cross-stream interference component of data signal impairment correlations. The use of like pre-coding weights, e.g., beam forming weights in D-TxAA MIMO, for different information streams further complicates the parametric modeling of impairment correlation estimation.
In short, the parametric modeling approach to impairment correlation estimation and compensation, such as used in G-Rake, chip equalizer, and other interference-suppressing receiver architectures, becomes problematic in MIMO systems. Even assuming that pilot-based observations provide a basis for determining impairment correlations in a received communication signal, the number of impairment contributors that are potentially significant is so large that model fitting becomes computationally burdensome and the fitting results become correspondingly less accurate as the number of model terms simultaneously fitted to measured impairment correlations increases.